• <tr id="yyy80"></tr>
  • <sup id="yyy80"></sup>
  • <tfoot id="yyy80"><noscript id="yyy80"></noscript></tfoot>
  • 99热精品在线国产_美女午夜性视频免费_国产精品国产高清国产av_av欧美777_自拍偷自拍亚洲精品老妇_亚洲熟女精品中文字幕_www日本黄色视频网_国产精品野战在线观看 ?

    Optimal Resource Allocation in Fog Computing for Healthcare Applications

    2022-08-23 02:22:30SalmanKhanIbrarAliShahNasserTairanHabibShahandMuhammadFaisalNadeem
    Computers Materials&Continua 2022年6期

    Salman Khan,Ibrar Ali ShahNasser Tairan,Habib Shah and Muhammad Faisal Nadeem

    1Department of Computer Software Engineering,University of Engineering and Technology,Mardan,23200,Pakistan

    2Department of Computer Science,College of Computer Science,King Khalid University,Abha,K.S.A

    3Informatics Complex,H-8,Islamabad,44000,Pakistan

    Abstract:In recent years,the significant growth in the Internet of Things(IoT)technology has brought a lot of attention to information and communication industry.Various IoT paradigms like the Internet of Vehicle Things (IoVT)and the Internet of Health Things (IoHT) create massive volumes of data every day which consume a lot of bandwidth and storage.However,to process such large volumes of data, the existing cloud computing platforms offer limited resources due to their distance from IoT devices.Consequently,cloudcomputing systems produce intolerable latency problems for latency-sensitive real-time applications.Therefore,a new paradigm called fog computing makes use of computing nodes in the form of mobile devices, which utilize and process the real-time IoT devices data in orders of milliseconds.This paper proposes workload-aware efficient resource allocation and load balancing in the fog-computing environment for the IoHT.The proposed algorithmic framework consists of the following components: task sequencing, dynamic resource allocation, and load balancing.We consider electrocardiography(ECG)sensors for patient’s critical tasks to achieve maximum load balancing among fog nodes and to measure the performance of end-to-end delay,energy,network consumption and average throughput.The proposed algorithm has been evaluated using the iFogSim tool,and results with the existing approach have been conducted.The experimental results exhibit that the proposed technique achieves a 45%decrease in delay,37%reduction in energy consumption,and 25%decrease in network bandwidth consumption compared to the existing studies.

    Keywords:Fog computing;internet of health things;resource management

    1 Introduction

    With the rapid advancements of smart devices and mobile communication technologies, the Internet of Things(IoT)has become a popular industry.According to an estimate by Cisco,more than 50 billion things have been expected to connect through the Internet by 2020[1].This massive growth in IoT devices will continue to increase exponentially in the ongoing decade,and the generated data will be enormously difficult to handle.Among them,the growing attention of thethingsis that the Internet of Health Things(IoHT)is increasing day by day.These things consist of sensors,smart devices,and many other sensors that produce an enormous amount of data to be processed quickly.All these data sent to the server for processing are in the form of tasks and are latency-sensitive.The tasks produce by IoHT devices are dynamic,stochastic,and variant in nature and require an immediate response.Cloud computing is a centralized computed paradigm that can process and store massive data generated by end devices and give them reliable services [2].However, end devices consume a lot of network bandwidth and burden cloud data centers, which creates communication latency [3].Consequently,many IoHT delay-sensitive services and applications for healthcare patients cannot be processed and responded quickly by cloud computing.Therefore,this disadvantage of cloud computing has brought an attention and give an emergence of new computing paradigm, called as fog computing [4].Fog computing has gained significance attention in recent years,and is an effective computing paradigm which renders services to IoT users and its related application domain at the edge of network[5,6].

    Fog computing utilizes nodes, such as mobile and IoT devices, by processing the real-time data generated by these devices.Typically, the response time of a fog computing system is very quick,which is quite beneficial for latency-sensitive applications.Fog computing is beneficial for real-time applications;among them,healthcare applications have gained immense popularity in recent years[6].However,fog computing is still in its infancy,and the devices are not so powerful to execute the request of different IoT devices.Resource management for the incoming request of various IoHT tasks is a challenging issue in fog computing.IoHT applications send patient’s critical data and tasks patient’s to the fog servers.These critical tasks need to be responded quickly without any delay.storage Fog nodes may forward this data to cloud data centers for processing and storage for long-term analytics.However,fog devices are generally constrained for resources,unlike cloud data centers.The processing and storage capacity of fog computing is limited in nature to fulfill the requirements of diverse and stochastic data[7].

    To fulfill the requirements of diverse and dynamic data arriving from end-users to resource constrained devices causes overhead delays and wastage of resources.The resource utilization in fog computing is a challenging issue that must be addressed [6–11].Proper resource allocation, nodes sorting according to task requirements, and load balancing are critical issues, affecting the fast and timely response desirable for several real-time applications.For instance, in IoHT healthcare critical applications,patients’information and appointment are of great importance to save their lives[4–9].Hence, considering the criticality of healthcare applications, there is a need to develop an efficient resource utilization of fog resources for diverse applications.This research article proposes a workload-aware dynamic resource allocation and load balancing algorithm for IoHT applications to achieve maximum resource utilization among various fog nodes.This algorithm ameliorates the traditional overhead-based scheduling algorithm for making appropriate resource allocation.The tasks are assigned as per the capacity of processing power.The primary objectives are to minimize the delay, energy, network consumption and maximize average throughput as performance metrics.Individually IoT application consists of fine-grained tasks,but the individual task has the following attributes:CPU response time and workload size.

    Thus, dynamic resource allocation and task scheduling in the distributed fog computing with consideration of optimal load balancing are investigated in the paper,which is different from existing works.We are analyzing the following challenges: (i) How to choose a dynamic resource allocation of IoHT tasks for optimal offloading?(ii)How to optimize the load balancing of fog devices during their peak workload?The paper makes the following key contributions:

    · The study devises the optimal resource allocation in a fog computing environment for the IoHT delay-sensitive applications.The proposed optimization algorithm consists of task sequencing,dynamic resource allocation, and load balancing.The nodes are parametrized and sorted according to tasks requirements and balances the load to reduce delays and ameliorate the wastage of resources.

    · This study considers the IoHT applications that have stringent requirements in terms of their execution and the resources required for their timely completion.Therefore, the study implements node sequencing according to tasks before tasks are assigned to them.The aim is to sort all nodes to allocate resources to tasks and execute them in an optimized way.

    · This study adopts dynamic changes in the fog computing environment for IoHT applications,an overhead-based dynamic scheduling algorithm has been suggested.The main goal of the algorithm is to allocate resources to reduce delays, energy, network consumption, execution time,and maximize the throughput.

    The rest of this paper is organized as follows; Section 2 highlights the related work; Section 3 presents the fog computing architecture;Section 4 covers the resource allocation and load balancing problem in fog computing; Section 5 presents the proposed resource-scheduling algorithm; Section 6 presents the experimental evaluation and analysis; Section 7 concludes the work highlights future direction.

    2 Related Work

    Several existing research works have proposed resource management techniques in fog computing,with specific focus on healthcare system.Resource management is a major concern of fog computing infrastructure designers.The purpose of resource management is to utilize the resource-constrained devices optimally to reduce latency, energy consumption, cost, network usage and provide better quality of service (QoS).Jamil et al.[4] have presented the performance optimization and job scheduling algorithm in fog computing for healthcare critical patients.The purpose of the proposed method is to reduce average delay and energy consumption as an evaluation metrics for delay-sensitive critical healthcare applications.However, they did not consider the dynamic resource allocation and load balancing which raises the problem of overhead delays and wastage of resources.Kumari et al.[6], Ijaz et al.[8], Awaisi et al.[9], Tuli et al.[10] present fog architecture for patient-oriented real-time healthcare applications for data collection,processing and transmission.The authors have highlighted existing issues in healthcare system, such as faulty data and data duplication, data integration, user authentication, data security and privacy.However, they only restricted their work towards healthcare security issues and exploited to mention the dynamic resource allocation and load balancing mechanism for proper resource utilization.Aazam et al.[11],Rahmani et al.[12]presented fog based architecture for emergency alert system and smart e-health gateways to tackle IoT related healthcare system issues.The primary objective of the proposed architecture in fog computing is to overcome the delay,complexity,scalability,mobility,interoperability,and reliability.However,proper resource allocation,and task scheduling are the main concern for healthcare critical applications to overcome delays,energy,network and CPU response time are exploited.Hassan et al.[13]proposed a fog computing-based remote pain monitoring system that collects electromyography(EMG)signals and processes it to detect the pain of different patients.In the proposed model,the information related to pain can be accessed remotely through web application using minimum time and enabled timely medical facilitation to the patients.However, they applied first come first serve (FCFS) scheduling algorithm, which lacks dynamic resource allocation and load balancing to optimize resources and reduce overhead delays.Lakhan et al.[14], Lakhan et al.[15], Lakhan et al.[16] presented fogcloud framework for Internet of Vehicle Things (IoVT) and Internet of Medical Things (IoMT).The proposed framework focus on cost-efficient task offloading and task scheduling algorithm in fog-cloud environment.The proposed algorithm framework cost-efficient mobility offloading and task scheduling (CEMOTS), deep neural networks energy cost-efficient partitioning and task scheduling (DNNECTS) and blockchain-enable smart-contract cost-efficient scheduling algorithm framework(BECSAF)consist of task offloading,task sequencing and scheduling.The main objectives are to optimize healthcare application costs and computational costs in the dynamic environment.However,they did not mention other parameters for consideration in healthcare related applications.Mohammad et al.[17],Abdulkareem et al.[18]proposed machine learning approaches for visual antispam to detect abnormalities.The proposed approach consists of na?ve bayes (NB), random forest(RF),and support vector machine(SVM)classifiers.The primary objectives of the proposed machine learning approaches are to reduce doctors workload, mortality rate, tackling overcrowding during COVID-19 pandemic.However, the proposed approaches is based on multi-natural language and COVID-19 pandemic situation and lacking the fog computing architecture which can be addressed.Ahmad et al.[19],Ahmad et al.[20]proposed multi-device and multi-task orchestration architecture for IoT enterprises.The purpose of the proposed architecture is to provide scalable and flexible operation from tasks generation to mapping and scheduling till allocating them on respective IoT resources in order to reduce round trip time and task dropping rate.The main objective of the optimization is to maximize the flexibility and scalability of the system for newly added devices.However, the proposed architectures mainly lack the concept of load balancing technique for fog-IoT environment.Ahmad et al.[21],Ahmad et al.[22]proposed an adaptive approach for real-time IoT job for formal verification.The proposed approach consider optimal threshold value for task set and provide real time environment for IoT tasks for monitoring and evaluation.The significance of the proposed work is to improve response time, CPU utilization and power consumption.However,the proposed work did not consider resource allocation and load balancing for tasks execution in fog computing.Shaheen et al.[23]proposed framework and present the concept of fog head node to keep track of other nodes in location-awareness and user registration perspective.The primary concern of the work is to reduce latency,network consumption and service time.However,the study did not consider the load balancing for delay-sensitive applications in fog-IoT environment.

    To the best of our knowledge, workload-aware dynamic resource allocation and load balancing algorithm for multi-objective optimization under CPU, network constraint has not been studied yet.All the studies mentioned earlier,either solve static resource allocations while ignoring resource utilization or use dynamic resource allocation while missing the load balancing.The overhead of dynamic allocation in resource-constraint devices directly affect real-time applications.Therefore,this study optimizes the utilization of resources for healthcare applications and reduces the overhead delay,energy,network consumption,and execution time and maximizes the average throughput as a performance metric.

    3 Proposed Fog Computing Architecture

    In this section, n-tier fog computing architecture has been described as depicted in Fig.1.The architecture consists of three layers,namely cloud layer,fog layer,and IoT layer.The proposed architecture is meant for delay-sensitive healthcare applications for efficient utilization of fog computing resources.The illustration of each layer in this architecture has been discussed below.

    Figure 1:Healthcare system architecture based on fog computing

    3.1 Cloud Layer

    The cloud-computing layer is the topmost layer of the architecture,as shown in Fig.1.The cloud layer consists of large data centers for massive data processing and storage purposes[24].It provides services to the end-users in various application domains,such as smart cities,smart transportations,smart health,smart production companies,and virtual reality.All these application areas are delaysensitive and require a quick response from the data center.However,due to the centralized nature and multi-hop distances from IoT devices[20,21],the issue of latency arises as a main challenging problem in cloud computing for these delay-sensitive applications and cannot meet real-time requirements.

    3.2 Fog Layer

    Fog layer lies in between the cloud layer and IoT layer.The fog layer comprises servers,routers,modems,PC’s etc.All the devices with the potential to process,store and communicate data can be considered as fog devices.Fog computing came into existence after the latency issues were faced by the cloud computing systems.Different application domains required faster response and fog computing meets the real time demands from IoT devices[25]and fulfill user satisfaction.

    3.3 IoT Layer

    The IoT layer is the bottom layer of the architecture, as shown in Fig.1.This layer consists of sensors and actuators to collect real-time data from the surroundings and send it to the fog layer for further processing.There are certain domains in which the IoT layer can play a vital role and improve the quality of service.These application areas include smart transportation,smart healthcare,industrial automation,and emergency response etc.[23–24].

    3.4 Schedular

    In this work,we introduce a schedular or decision-maker module,which is depicted in Fig.1.This module is significant in fog computing resource management, as it balances the incoming requests or loads equally into fog servers.This technique gives the surety that no overloaded servers will remain in the system.The main advantage of using this technique is to maximize resource utilization,increase throughput,minimize the delay and waiting time and improve overall system reliability and performance.

    4 Resource Allocation and Load Balancing Problem in Fog Computing

    Resource allocation and load balancing is a persistent problem related to cloud computing and fog computing.If resource allocation is performed in cloud computing,then large data transmission consumes massive bandwidth and causes traffic burden on cloud data centers,and ultimately creates delays for delay-sensitive tasks.On the other hand, resource allocation and load balancing in fog computing are also significant issues because the devices in the fog computing environment are weaker in terms of their processing capability and storage capacity,which need to be addressed by applying appropriate techniques.

    This study simulates the workload aware efficient dynamic resource allocation and load balancing problem in fog computing for healthcare applications.The goal is to optimize resource utilization,thereby reducing end-to-end delay,energy,network consumption,and execution time for applications.The goal is also to achieve the average throughput utility by applying the proposed technique.

    5 Design and Implementation

    Real-time applications are delay-sensitive and need faster response time.Cloud computing systems are centralized and geographically far away from end-users; therefore, it produces communication latency and is unable to address IoT applications’needs.Fog computing emerges as a new computing paradigm and provides essential services to end-user applications to address the issue.However,fog computing resources are limited as compared to cloud computing and the IoT devices send various tasks that are variable in length and stochastic;therefore,to manage the various nondeterministic tasks in the resource-constrained fog devices,various resource management techniques have been applied.This article presents a workload-aware dynamic resource allocation and load balancing technique for healthcare applications to optimize various performance metrics.The proposed approach aims to reduce the delay and energy,network consumption,and maximize the average throughput for critical healthcare applications.

    Therefore,there is a need to design and implement a task-scheduling algorithm in fog computing for healthcare applications with the following objectives:

    · To efficiently utilize fog resources.

    · To minimize the end-to-end delay(Healthcare application).

    · To minimize energy consumption,network consumption,and execution time.

    · To maximize the average throughput utility.

    5.1 Case Study

    IoT devices consist of different sensors, actuators, smart gadgets etc., which allow monitoring of different activities for the healthcare system [26].Fog computing is an emerging technology and is an essential architectural paradigm for Ubiquitous computing.This article presents a case study for critical healthcare applications in fog computing for different activities.These activities are delaysensitive and can be performed by fog computing for faster response and more minor delays.

    In healthcare,different use case classes have distinct requirements.Some classes are more critical,while some afford to observe delay.Patients require faster response and prompt data analysis to save their lives in a critical or emergency situation.In our healthcare scenario,we use three use cases to find the average delay,which have been elaborated as follows:

    a)Emergency Alert System.Emergency alert processes the patient’s vital data like heartbeat,blood pressure, and blood glucose level obtained from various body sensors.This use case contains patient’s critical data to be processed in case of emergency.The data is analyzed and generates timely notifications of patient’s health to save their lives.

    b)Patient Appointment System:This module gets an appointment for patient’s,and also ensures the elimination of appointment duplication for the various patients.

    Yes, we did. We had another argument over candy in the frozen5 yogurt. Of course we laughed through that argument as well. That s when I knew that this guy was special. He was strong, yet tender6.

    c) Patient Record System:This use case contains vital information about patient’s personal information store in the database.Besides patient’s information,it includes doctor information,doctor visit, patient treatment, and lab results.This module also provides for the record of newly admitted patients.

    To better recognize this healthcare case study, similar to the work [4], this article uses three application modules in the fog-computing framework.

    A.DCPB:Data processing and communication board module(DCPB)receives data from the IoHT layer.The tasks are considered critical and non-critical.Critical data such as various sensors send crucial tasks for immediate response and is notified.It receives all the data for the patient’s appointment and records but forwards it to the organizer module.The primary concern of this module is to receive all tasks generated from the IoHT layer to sequenced them and forward them further for necessary processing.It also sends back the notification response after processing from an organizing module.

    B.Organizer:This module acts as a high-level fog device that receives data from the DCPB module.The working nodes are sorted according to the requirements of tasks.The applied algorithm is designed to allocate resources to tasks and check the overhead if any occurs.The fog nodes are continuously checked for CPU overhead; if there found any node that is not capable of executing the request or the node is over-utilized,then the load balancing module will balance the load from over-utilized node to less utilized node,and this process continues until all the tasks are fully executed.

    C.Patients Record Database:This module receives data from the organizer and sent to the cloud for long-term analytics and storage.The module saves the patients record,which is noncritical, and generates reports sent back to the organizer module about patient’s health and hospital visit information.All the modules used in our case study are shown in Fig.2.

    In this paper, we present a systematic approach to control task submission under the optimal threshold to guarantee the successful execution of tasks within their time constraints.The optimal threshold is selected based on the number of fog nodes resources.Moreover,the paper has three main parts:first is the task mapping to appropriate devices,the devices are sorted according to tasks,second the resources are allocated to variant tasks.If there are unscheduled tasks having no response from node resources or some of the resources are underutilized,then we apply a load balancing technique for the optimal utilization of resources in order to reduce the overhead, energy, and response time.Fig.3 shows the overall flowchart of the proposed approach.

    Figure 2:Proposed healthcare framework in n-tier computing

    Figure 3:Flow diagram of the proposed approach

    The proposed algorithm dynamically allocates resources to healthcare tasks and balances the load among fog devices to minimize the delay, energy, and network utility.This algorithm searches concurrently in all fog nodes for an available resourcerto serveUTiwithin the required response time.If the start time ofUTi,when served byr,is greater than the earliest start time ofUTi,then the task will be served in its original fog nodeNdk.Otherwise,the algorithm will shift or migrateUTito other fog node that contains resourcer,and scheduleUTitor.The maximum utilization of the resourcerhas been set as 5000 mips threshold value.The pseudocode of the proposed algorithm in this article is given as follows:

    Algorithm 1:Workload-Aware Dynamic Resource Allocation and Load Balancing Algorithm Input:Task list Ti(Ti|i=1,2, ...,n),Node list Ndk(Ndk|k=1,2, ...,r)Output:Min[D,E,N],Max[Tr]Initialization:CUr current utilization of resource r.MUr maximum utilization of resource r.UTim mips required by unscheduled task Rm mips available with the resource r.Rt is the time available by the resource for UTim Tiest is the earliest stating time by task Ti Rj is the set of fog computing resources in fog nodes Ndk that can serve UTi 1 For each(Ti ∈n)do 2 For each(Ndk ∈r)do 3 Sort all nodes r based on tasks n 4 Allocate every Ti ∈n to Ndk ∈r 5 If(CUr <MUr)then 6 If(UTim <=Rm and Rt <=Tiest)then 7 Shift UTi to j 8 Schedule UTi to R 9 Update Rm 10 Update Rt 11 UTi as scheduled 12 Break 13 End If 14 End If 15 End For 16 End For

    For the implementation of our proposed algorithm,iFogSimwas chosen because it is an extension ofCloudSim.iFogSimis used for modeling and simulation of cloud-fog computing architecture as well as services and efficient for simulation of resource management techniques for Fog IoT environment.

    6 Performance Evaluation

    We present the performance of our proposed algorithm and evaluate it based on delay, energy consumption,network usage,and throughput.

    6.1 Experimental Settings

    Cloud and fog have different processing capacities and other resources.We assumed that each node has its own processing capacity represented by million instructions per second (MIPS), along with memory and bandwidth usage.In the fog layer,fog nodes could be routers,gateways,workstations,or personal computers with limited processing capacity compared to the Cloud layer,which has servers or virtual machines in high-performance data centers and is responsible for handling tasks.Therefore,the processing speed of Cloud nodes is much faster than fog nodes.Thus, the characteristics of fog computing in the case of our healthcare scenario are presented in Tab.1.The parametric values are arbitrary, but we have considered the limitations of a fog environment as compared to a cloud.We implemented the proposed algorithm iniFogSimand utilized these parameters to generate our simulation results.

    Table 1: Value of parameters used for cloud fog-based simulation framework

    6.2 Simulation Setup

    The settings of the simulations setup are presented in Tab 2, and the simulation program was developed in Java with Eclipse editor usingiFogSim[27].iFogSimwas chosen because it is has been implemented as an extension of CloudSim,and hence inherits several features from CloudSim.

    Table 2: Software/Hardware configuration

    6.3 Evaluation Results

    6.3.1 Loop Delay

    We used this metric to measure the end-to-end loop delay.To compute the loop delay, we first determine the execution delay of each computed as follows[4]:

    Whereαirepresents the starting time of execution andβirepresents the ending time of execution forithtasks.The average CPU time taken by all the tasks and a time taken by a particular task is computed as follows:

    WhereNrepresents the total number of executable tasks in task set T.Fig.4 shows the computed loop-delay experienced by all applications and is measured in milliseconds.In Fig.4,the number of nodes is represented on the horizontal axis,while the application loop delays are represented on the vertical axis.

    Dynamic resource allocation and load balancing are an important methods for the optimization of system performance during application provisioning.However,the methods are widely ignored by the existing studies Fig.4 demonstrate that proposed algorithm optimises the system performance by applying dynamic resource allocation and load balancing methods during application provisioning.This ensures the appropriate utilization of resources to reduce the overhead delay,energy and network consumption of nodes.

    The main advantage of this technique is the appropriate utilization of fog computing resources which have a significant impact on system paramenters.The study proved that simulation results,as shown in Fig.4, gained optimal performance in dynamic resource allocation and load balancing method and the overall end-to-end delay of the applications are improved in the proposed algorithm framework.

    6.3.2 Energy Consumption

    The energy consumption of a fog device is computed as follows:

    WherePidle,Pmax,andcmaxare represented as idle power,maximum power,and maximum capacity.The value of the termPidlecannot be ignored because it consumes a massive amount of energy.

    The energy consumption has a great impact on system performance and usually the energy consumption of devices remains stable during static allocation.However, the dynamic and runtime environment,it creates a huge significance during task provisioning and resource allocation.The study proved that simulation results gained optimal performance of system during dynamic resource allocation and load balancing as shown in Fig.5.The overall energy consumption of IoHT applications are improved in the proposed algorithm.

    Figure 4:Loop delay for(a)emergency alert system(b)patients appointment system(c)patients record system(d)history of the number of nodes

    6.3.3 Network Consumption

    We evaluate the network consumption asηcon.The network consumption increase proportionaly by increasing the number of devices,and thus it causes network congestion.Cloud computing has poor network performance due to heavy network congestion.Fog computing reduces network congestion by distributing the load among fog nodes.This study proposed dynamic resource allocation and load balancing algorithm for maximum utilization of fog resources.The primary goal of the proposed work is to reduce network burden on nodes.The mathematical equation of network consumption is derived as follows[4]:

    Where N represents the number of tasks,Lirepresents the latency,and Niis used for network size ofithtask.Fig.4 shows the comparison result of network consumption of our proposed technique with first come first serve(FCFS)and shortest job first(SJF)algorithms.

    Figure 5:Average energy consumption in[J]by varying the number of fog nodes

    As the number of requests increases,the network consumption also increases due to limited nodes.However,the dynmic resource allocation and load balancing algorithm makes the proper utilization of fog resources and the simulation results prove that the network consumption of the proposed algorithm are improved as depicted in Fig.6.

    Figure 6:Network consumption in[MB]

    6.3.4 Average Throughput

    Average throughputT Pavgis measured as the total task executed by fog devices per unit time.Therefore,the average throughput utility for the number of fog devices is.

    Whereαiis the task execution by fog server forithtask,γ iis the ithtask admitted in fog queue,and′nis the normalization parameter in the utility function.

    The average throughput utility increases with the increase of fog devices executing the IoT tasks.The graph tilted a little as the nodes from 100 to 200,but it increased when the nodes also increased from 200 to 500.Therefore,as the fog devices increase,they are in a better position to execute as many tasks as possible as shown in Fig.7.

    Figure 7:Average throughput utility of Fog devices

    This work has some limitations related to healthcare applications and systems, which can be improved in future work.(i)This work did not consider the security aspect for user satisfaction.(ii)This work lacks the mechanisms to find the cost model i.e.,computational and application costs.(iii)This work did not consider the run time failure occurrence of any node or tasks.(iv)To find out the uncertainty of applications in a large searching space,this algorithm lacks the said problem.All of the mentioned limitations should be improved in future work.

    6.3.5 Results and Discussion

    Various IoT sensors generate different IoHT applications.Initially,the tasks are sequenced,and all the fog nodes are sorted according to the requirements of IoHT tasks.The three modules which we represented in our case study are the main working modules.The proposed algorithm is designed to make sure of the participating modules.The existing studies ignore dynamic resource allocation and load balancing mechanisms,which ultimately cause overhead delays,wastage of resources,and high energy and network consumption.The key feature of the proposed algorithm is to utilize fog resources as many as possible and reduced the overall impact of resource utilization versus IoHT applications in terms of end-to-end delay,energy,network consumption and execution time.This algorithm also features evidence of achieving the maximum throughput,which is an additional finding of our study.After obtaining the results,the proposed algorithm performs much better compared to the algorithms used in previous work.The obtained results for healthcare applications were achieved under given conditions using iFogSim.

    6.3.6 Performance Comparison

    The performance of the proposed work compared with recent related work published in the literature is listed in Tab.3.The comparison is made based on delay,energy and network consumption,execution time,average throughput as performance metrics for research consideration.

    Table 3: Comparison table

    7 Conclusion and Future Work

    This paper discussed the dynamic resource allocation and load balancing of IoHT applications in fog computing environment.In this regard, we proposed a smart and appropriate technique,workload-aware dynamic resource allocation,and load balancing algorithm framework.The proposed algorithm consists of different components, such as task sequencing, dynamic resource allocation,and load balancing.Initially, the study designed the setup of an n-tier simulation environment for various IoHT applications.Subsequently,we implemented the proposed resource allocation and load balancing technique in the experimental setting and compared them with the existing methods.The results and discussion showed that the proposed approach outperforms the existing techniques in terms of delays, energy, network consumption, execution time, and average throughput utility.The proposed work considers the healthcare case study using three modules.These modules mainly focus on task sequencing,dynamic resource allocation,and load balancing for delay-sensitive applications to optimize the overall system performance, as shown in results and discussion.There are a few limitations of the current study,such as finding the overall computational cost.In addition,the security and privacy are also the limitations of the current study.

    Besides the current implication of using the proposed technique for healthcare applications,the main advantage of using the proposed method under the given healthcare scenario is that it will reduce the delay or patients’request response to save their lives and mortality.This method can be considered for other application domains like smart cities, Internet of vehicular things and smart farming purposes.

    In the future,we will consider the problems mentioned earlier and use heuristic and meta-heuristic optimization techniques for dynamic workload healthcare scenarios.The current application scenario considers critical,less critical,and non-critical tasks.The future work focuses mainly on more critical tasks to optimally utilize the fog resources and minimize delay,energy,and response time.

    Funding Statement:This research is supported and funded by King Khalid University of Saudi Arabia under the Grant Number R.G.P.1/365/42.

    Conflicts of Interest:The authors declare that they have no conflicts of interest to report regarding the present study.

    午夜精品在线福利| 国产精品av视频在线免费观看| 欧美日韩精品成人综合77777| 美女主播在线视频| 男的添女的下面高潮视频| 亚洲精品成人av观看孕妇| 亚洲国产精品成人综合色| 五月玫瑰六月丁香| 国产极品天堂在线| 免费看光身美女| 欧美一级a爱片免费观看看| 欧美日韩视频高清一区二区三区二| 亚洲精品色激情综合| 国产一区二区三区av在线| 亚洲精品aⅴ在线观看| 国产精品1区2区在线观看.| 十八禁国产超污无遮挡网站| 韩国av在线不卡| 久久精品国产亚洲av天美| 亚洲精品国产av成人精品| 久久99热这里只频精品6学生| 床上黄色一级片| 国产伦精品一区二区三区视频9| 国产一级毛片七仙女欲春2| 在线观看一区二区三区| 免费观看性生交大片5| 五月天丁香电影| 国产亚洲av嫩草精品影院| 亚洲欧美中文字幕日韩二区| 久久草成人影院| 久久久久精品性色| 欧美成人午夜免费资源| 久久久久国产网址| 国产精品av视频在线免费观看| 青春草亚洲视频在线观看| 男插女下体视频免费在线播放| 成人二区视频| 国产精品.久久久| 午夜福利在线在线| a级毛片免费高清观看在线播放| 欧美xxxx黑人xx丫x性爽| 99热6这里只有精品| 乱码一卡2卡4卡精品| 中国美白少妇内射xxxbb| 免费观看av网站的网址| 久久国产乱子免费精品| 久久久精品欧美日韩精品| 最近手机中文字幕大全| 久久久久久久大尺度免费视频| 午夜精品一区二区三区免费看| 国产熟女欧美一区二区| 美女国产视频在线观看| 色播亚洲综合网| 免费在线观看成人毛片| 成人美女网站在线观看视频| 免费看不卡的av| 蜜桃久久精品国产亚洲av| 成人一区二区视频在线观看| 午夜福利在线观看免费完整高清在| 日日摸夜夜添夜夜添av毛片| 亚洲欧美日韩东京热| 只有这里有精品99| 久久久午夜欧美精品| 国产亚洲91精品色在线| 久久人人爽人人片av| 精品一区二区三卡| 大话2 男鬼变身卡| 亚洲av免费在线观看| videossex国产| 晚上一个人看的免费电影| 80岁老熟妇乱子伦牲交| av网站免费在线观看视频 | 只有这里有精品99| 国产精品爽爽va在线观看网站| 春色校园在线视频观看| 老师上课跳d突然被开到最大视频| 日韩国内少妇激情av| av网站免费在线观看视频 | 久久韩国三级中文字幕| 欧美极品一区二区三区四区| 人妻制服诱惑在线中文字幕| 亚洲经典国产精华液单| 国产精品av视频在线免费观看| 国产男人的电影天堂91| 亚洲va在线va天堂va国产| 免费在线观看成人毛片| 97超碰精品成人国产| 欧美区成人在线视频| 少妇被粗大猛烈的视频| 亚洲av.av天堂| 午夜亚洲福利在线播放| 国产精品爽爽va在线观看网站| 又粗又硬又长又爽又黄的视频| 男人舔女人下体高潮全视频| 久久久午夜欧美精品| 亚洲精品视频女| 国产精品久久久久久久久免| 精品久久久久久久久亚洲| 哪个播放器可以免费观看大片| 在线观看av片永久免费下载| 精品不卡国产一区二区三区| 久久久久免费精品人妻一区二区| av在线亚洲专区| 大香蕉久久网| 男女啪啪激烈高潮av片| 一级毛片 在线播放| 一个人免费在线观看电影| 精品一区二区三区视频在线| 丝瓜视频免费看黄片| 91精品一卡2卡3卡4卡| 熟女电影av网| 亚洲欧美精品专区久久| 欧美xxⅹ黑人| 麻豆久久精品国产亚洲av| 91午夜精品亚洲一区二区三区| 亚洲av二区三区四区| 精品国产露脸久久av麻豆 | 日本爱情动作片www.在线观看| 舔av片在线| 性插视频无遮挡在线免费观看| 日韩亚洲欧美综合| 国产 一区精品| 免费av观看视频| 成人一区二区视频在线观看| 国产在视频线在精品| 一级爰片在线观看| 免费黄网站久久成人精品| 精品久久久久久成人av| 国产又色又爽无遮挡免| 男女啪啪激烈高潮av片| xxx大片免费视频| 国产成人精品婷婷| 春色校园在线视频观看| 老女人水多毛片| av天堂中文字幕网| 美女黄网站色视频| 久久久久网色| 久久精品国产鲁丝片午夜精品| 成人午夜精彩视频在线观看| 国产极品天堂在线| 国产精品精品国产色婷婷| 免费黄色在线免费观看| 欧美成人精品欧美一级黄| 99久久人妻综合| 日本欧美国产在线视频| 3wmmmm亚洲av在线观看| 777米奇影视久久| 亚洲精品色激情综合| 舔av片在线| 嫩草影院新地址| 最后的刺客免费高清国语| 精品一区二区三区视频在线| 在线 av 中文字幕| 水蜜桃什么品种好| 18禁动态无遮挡网站| 国产色爽女视频免费观看| 亚洲久久久久久中文字幕| 熟妇人妻久久中文字幕3abv| 日韩制服骚丝袜av| 欧美日韩国产mv在线观看视频 | 国产精品国产三级专区第一集| 成人特级av手机在线观看| 精品人妻熟女av久视频| 在线播放无遮挡| 汤姆久久久久久久影院中文字幕 | 午夜精品在线福利| 国产一区有黄有色的免费视频 | 男人舔奶头视频| 超碰av人人做人人爽久久| 丝瓜视频免费看黄片| 黄色一级大片看看| 国产伦精品一区二区三区视频9| 一本久久精品| 亚洲在久久综合| eeuss影院久久| 欧美激情在线99| 蜜桃久久精品国产亚洲av| 一级毛片黄色毛片免费观看视频| 久久精品综合一区二区三区| 午夜福利在线观看吧| 国产91av在线免费观看| 高清午夜精品一区二区三区| 嫩草影院精品99| 日韩一区二区三区影片| 国产精品不卡视频一区二区| 亚洲精品亚洲一区二区| 日韩av在线大香蕉| 国产精品人妻久久久久久| 亚洲真实伦在线观看| av在线天堂中文字幕| 麻豆av噜噜一区二区三区| 久久久久免费精品人妻一区二区| 精品午夜福利在线看| 五月天丁香电影| 成年女人在线观看亚洲视频 | 久久草成人影院| av在线老鸭窝| 男的添女的下面高潮视频| 最新中文字幕久久久久| av一本久久久久| 免费在线观看成人毛片| 大香蕉久久网| 高清日韩中文字幕在线| 久久精品夜色国产| 欧美激情国产日韩精品一区| 国产老妇女一区| 2021天堂中文幕一二区在线观| 亚洲欧洲日产国产| av线在线观看网站| 国产熟女欧美一区二区| 亚洲国产欧美人成| 亚洲精品中文字幕在线视频 | 亚洲国产精品成人综合色| 久久97久久精品| 六月丁香七月| 一级黄片播放器| 久久精品熟女亚洲av麻豆精品 | 精品亚洲乱码少妇综合久久| 三级经典国产精品| 搡老乐熟女国产| 内地一区二区视频在线| 国产一区二区三区综合在线观看 | 淫秽高清视频在线观看| 天堂影院成人在线观看| 亚洲精品乱码久久久久久按摩| 欧美成人a在线观看| 国产 一区 欧美 日韩| 国产免费一级a男人的天堂| 精品久久国产蜜桃| 蜜桃久久精品国产亚洲av| 成人毛片60女人毛片免费| 一级二级三级毛片免费看| 午夜福利成人在线免费观看| 国产有黄有色有爽视频| 嫩草影院精品99| 在线观看人妻少妇| 亚洲欧美日韩无卡精品| 国产在视频线精品| 亚洲无线观看免费| 国产精品人妻久久久影院| 简卡轻食公司| 九草在线视频观看| av一本久久久久| 91精品国产九色| 精品久久久久久久人妻蜜臀av| 亚洲av一区综合| 91精品伊人久久大香线蕉| 日韩欧美国产在线观看| 欧美三级亚洲精品| 九色成人免费人妻av| 人妻少妇偷人精品九色| 可以在线观看毛片的网站| 肉色欧美久久久久久久蜜桃 | 亚洲av男天堂| 一级毛片我不卡| 午夜精品一区二区三区免费看| 国产免费福利视频在线观看| 国产亚洲午夜精品一区二区久久 | 在现免费观看毛片| 国产久久久一区二区三区| 免费无遮挡裸体视频| 婷婷六月久久综合丁香| 午夜福利在线观看吧| 直男gayav资源| 欧美日韩一区二区视频在线观看视频在线 | 麻豆成人av视频| 精品午夜福利在线看| 又爽又黄无遮挡网站| 欧美精品一区二区大全| 免费观看的影片在线观看| 看免费成人av毛片| 视频中文字幕在线观看| 在线播放无遮挡| 国产精品人妻久久久影院| 中文字幕免费在线视频6| 亚洲精品乱码久久久久久按摩| 国产在视频线精品| 久久久久国产网址| 一个人免费在线观看电影| 美女被艹到高潮喷水动态| 免费播放大片免费观看视频在线观看| 久久草成人影院| 中文字幕av成人在线电影| 亚洲va在线va天堂va国产| 十八禁网站网址无遮挡 | 色5月婷婷丁香| 亚洲av福利一区| 99久久人妻综合| 深夜a级毛片| 老司机影院成人| 欧美最新免费一区二区三区| 久久6这里有精品| 欧美高清成人免费视频www| 国产精品人妻久久久影院| 国产久久久一区二区三区| 亚洲欧美日韩无卡精品| 久久精品久久久久久久性| 久久久久久九九精品二区国产| 国语对白做爰xxxⅹ性视频网站| 久久鲁丝午夜福利片| 激情 狠狠 欧美| 国产成人精品一,二区| 久久这里只有精品中国| 亚洲久久久久久中文字幕| 亚洲自拍偷在线| 狂野欧美白嫩少妇大欣赏| 国产成人精品久久久久久| 亚洲婷婷狠狠爱综合网| 大香蕉久久网| 亚洲aⅴ乱码一区二区在线播放| 人人妻人人看人人澡| 又粗又硬又长又爽又黄的视频| 高清在线视频一区二区三区| 精品久久久久久久久亚洲| 韩国av在线不卡| 国产精品综合久久久久久久免费| 春色校园在线视频观看| 国产精品久久久久久久电影| 国产精品久久视频播放| 久久久久久久久久人人人人人人| 亚洲aⅴ乱码一区二区在线播放| 国产伦理片在线播放av一区| 欧美性猛交╳xxx乱大交人| 99久久精品一区二区三区| 亚洲18禁久久av| 精品久久久久久久久久久久久| 看非洲黑人一级黄片| 女人被狂操c到高潮| 午夜日本视频在线| 乱系列少妇在线播放| 亚洲欧美精品自产自拍| 亚洲av免费高清在线观看| 波野结衣二区三区在线| av国产久精品久网站免费入址| 九草在线视频观看| 一个人免费在线观看电影| 国产精品不卡视频一区二区| 舔av片在线| 午夜免费男女啪啪视频观看| 亚洲欧洲日产国产| 大又大粗又爽又黄少妇毛片口| 日韩大片免费观看网站| 国产 一区 欧美 日韩| 精品久久久久久电影网| 亚洲精品一区蜜桃| 国产亚洲av嫩草精品影院| 伦理电影大哥的女人| 嫩草影院新地址| 中国美白少妇内射xxxbb| 一级毛片电影观看| 午夜免费男女啪啪视频观看| 婷婷色综合www| 亚洲无线观看免费| 在线观看美女被高潮喷水网站| 午夜久久久久精精品| 亚洲精品自拍成人| 一区二区三区四区激情视频| 高清在线视频一区二区三区| 1000部很黄的大片| 最近视频中文字幕2019在线8| 99热这里只有是精品50| 欧美精品国产亚洲| 美女被艹到高潮喷水动态| 国产一区亚洲一区在线观看| 成人综合一区亚洲| 国产精品三级大全| 欧美高清成人免费视频www| 久久久精品94久久精品| 免费av观看视频| a级毛片免费高清观看在线播放| 久久这里有精品视频免费| 777米奇影视久久| 成人美女网站在线观看视频| 亚洲自偷自拍三级| 欧美97在线视频| 国产成人精品婷婷| 欧美97在线视频| 亚洲av成人av| 国产激情偷乱视频一区二区| 噜噜噜噜噜久久久久久91| 免费看不卡的av| 狂野欧美激情性xxxx在线观看| 免费少妇av软件| 3wmmmm亚洲av在线观看| 免费av毛片视频| or卡值多少钱| 久久久久久久国产电影| 男的添女的下面高潮视频| 亚洲欧美日韩卡通动漫| 老司机影院成人| 久久久精品免费免费高清| 少妇被粗大猛烈的视频| 高清欧美精品videossex| kizo精华| 久久99热这里只有精品18| 熟女人妻精品中文字幕| 少妇高潮的动态图| 色综合站精品国产| 国产在视频线在精品| 精品人妻视频免费看| 97精品久久久久久久久久精品| 久久久亚洲精品成人影院| 2021少妇久久久久久久久久久| 国产成人精品久久久久久| 国产亚洲精品久久久com| 大话2 男鬼变身卡| 51国产日韩欧美| 蜜臀久久99精品久久宅男| 建设人人有责人人尽责人人享有的 | 国产精品国产三级专区第一集| 亚洲四区av| 99久久九九国产精品国产免费| 爱豆传媒免费全集在线观看| 三级毛片av免费| 国产成人freesex在线| a级毛片免费高清观看在线播放| 国产亚洲av片在线观看秒播厂 | 亚洲高清免费不卡视频| 成年女人在线观看亚洲视频 | 久久久久久国产a免费观看| 国产单亲对白刺激| 国产午夜福利久久久久久| 校园人妻丝袜中文字幕| 午夜视频国产福利| 亚洲精品乱久久久久久| 欧美极品一区二区三区四区| ponron亚洲| 亚洲va在线va天堂va国产| 搞女人的毛片| 性插视频无遮挡在线免费观看| 国产精品一区二区在线观看99 | 亚洲,欧美,日韩| 日日干狠狠操夜夜爽| 亚洲色图av天堂| 在线观看免费高清a一片| 欧美区成人在线视频| 男人和女人高潮做爰伦理| 久久热精品热| 成人鲁丝片一二三区免费| av国产免费在线观看| av线在线观看网站| 日韩欧美三级三区| 成人综合一区亚洲| 久久久久久久午夜电影| 十八禁网站网址无遮挡 | 黄色一级大片看看| 亚洲高清免费不卡视频| 1000部很黄的大片| 国产乱来视频区| 97人妻精品一区二区三区麻豆| 欧美高清性xxxxhd video| 亚洲一级一片aⅴ在线观看| 一级毛片aaaaaa免费看小| 听说在线观看完整版免费高清| 日韩,欧美,国产一区二区三区| 国产美女午夜福利| 三级男女做爰猛烈吃奶摸视频| 国产在视频线精品| 日韩av不卡免费在线播放| 干丝袜人妻中文字幕| 国产一区二区在线观看日韩| 日本一本二区三区精品| 亚洲最大成人av| 成人高潮视频无遮挡免费网站| 国模一区二区三区四区视频| 久久热精品热| 嘟嘟电影网在线观看| 精品一区二区免费观看| av专区在线播放| 天美传媒精品一区二区| 国产男女超爽视频在线观看| 成人av在线播放网站| 亚洲成人精品中文字幕电影| 中文字幕人妻熟人妻熟丝袜美| 亚洲内射少妇av| 人妻一区二区av| 六月丁香七月| 久久久久久九九精品二区国产| 美女高潮的动态| 99久久精品热视频| 国产伦精品一区二区三区四那| 日韩成人av中文字幕在线观看| 欧美最新免费一区二区三区| 久99久视频精品免费| 少妇人妻一区二区三区视频| 中文字幕人妻熟人妻熟丝袜美| 色哟哟·www| 久久精品国产亚洲av涩爱| 精品久久久精品久久久| 亚洲精品自拍成人| 熟妇人妻不卡中文字幕| 一级片'在线观看视频| 久久久久免费精品人妻一区二区| 美女内射精品一级片tv| 边亲边吃奶的免费视频| 免费看av在线观看网站| 日韩大片免费观看网站| 国产精品不卡视频一区二区| videossex国产| 精品人妻偷拍中文字幕| 亚洲精品国产av蜜桃| 亚洲18禁久久av| 色5月婷婷丁香| av国产久精品久网站免费入址| 午夜福利高清视频| 亚洲av电影不卡..在线观看| 国产成人精品婷婷| 亚洲最大成人中文| 国产精品嫩草影院av在线观看| 2021天堂中文幕一二区在线观| 亚洲精品一区蜜桃| 日韩强制内射视频| av福利片在线观看| 国产中年淑女户外野战色| 3wmmmm亚洲av在线观看| 日韩电影二区| 亚洲欧美日韩卡通动漫| 国产精品一区二区性色av| 成年免费大片在线观看| 美女黄网站色视频| 国产伦在线观看视频一区| 丰满少妇做爰视频| 蜜臀久久99精品久久宅男| 色尼玛亚洲综合影院| 国产精品三级大全| 女人被狂操c到高潮| 高清日韩中文字幕在线| 中国美白少妇内射xxxbb| 97热精品久久久久久| 日韩精品青青久久久久久| 欧美成人午夜免费资源| 国产黄a三级三级三级人| 九草在线视频观看| 国产精品一区www在线观看| 2018国产大陆天天弄谢| 男女边摸边吃奶| 熟女人妻精品中文字幕| 日韩欧美 国产精品| 成年人午夜在线观看视频 | 亚洲精品日韩在线中文字幕| 国产精品国产三级国产专区5o| 中文天堂在线官网| 成人av在线播放网站| 午夜日本视频在线| 亚洲成人一二三区av| 五月伊人婷婷丁香| 九九爱精品视频在线观看| 国产成人免费观看mmmm| 欧美一级a爱片免费观看看| 国产精品三级大全| 欧美高清成人免费视频www| 免费看美女性在线毛片视频| 日韩,欧美,国产一区二区三区| 一个人观看的视频www高清免费观看| 色网站视频免费| 99久国产av精品国产电影| 国内少妇人妻偷人精品xxx网站| 麻豆乱淫一区二区| 美女主播在线视频| 直男gayav资源| 精品午夜福利在线看| 赤兔流量卡办理| 午夜福利在线观看吧| 国产亚洲5aaaaa淫片| 男女那种视频在线观看| 啦啦啦中文免费视频观看日本| 成人毛片a级毛片在线播放| 亚洲久久久久久中文字幕| 国产精品三级大全| 国产高清不卡午夜福利| 欧美丝袜亚洲另类| 亚洲精品色激情综合| 精品久久久久久久人妻蜜臀av| 听说在线观看完整版免费高清| 白带黄色成豆腐渣| 国产乱来视频区| 黑人高潮一二区| 18+在线观看网站| 亚洲aⅴ乱码一区二区在线播放| av.在线天堂| 欧美日韩亚洲高清精品| 一级毛片电影观看| av.在线天堂| 亚洲av电影不卡..在线观看| 亚洲精品国产成人久久av| 夜夜看夜夜爽夜夜摸| 特大巨黑吊av在线直播| 精品一区在线观看国产| 免费观看在线日韩| 99热这里只有精品一区| 亚洲av电影在线观看一区二区三区 | 国产精品一区二区性色av| 国产91av在线免费观看| 嫩草影院精品99| 精品一区二区三区人妻视频| 丝瓜视频免费看黄片| 欧美一区二区亚洲| 午夜福利网站1000一区二区三区| 精品99又大又爽又粗少妇毛片| 久久久精品欧美日韩精品| 国产免费福利视频在线观看| 免费黄网站久久成人精品| 一级二级三级毛片免费看| 永久免费av网站大全| 亚洲久久久久久中文字幕| 99九九线精品视频在线观看视频| 成人毛片60女人毛片免费| 亚洲精品一区蜜桃| 日本av手机在线免费观看| 亚洲精品亚洲一区二区| 一级爰片在线观看| 性插视频无遮挡在线免费观看| 黄片无遮挡物在线观看| 国产激情偷乱视频一区二区| 男插女下体视频免费在线播放|